Lec 04: Heuristic Search

Introduction

This tutorial focuses on the fundamentals of heuristic search as presented in the lecture by Prof. Shyamanta M. Hazarika from IIT Guwahati. Heuristic search is a crucial concept in artificial intelligence, enabling efficient problem-solving by using strategies to guide the search process. This guide will break down the key principles and methods associated with heuristic search, making it accessible for students and practitioners in the field.

Step 1: Understand Heuristic Search

• Definition: Heuristic search refers to strategies used to make decisions or solve problems more efficiently when traditional methods are too slow.
• Purpose: It aims to find optimal solutions in complex search spaces by using heuristics—rules of thumb that reduce the search effort.
• Applications: Commonly used in pathfinding algorithms, game AI, and optimization problems.

Step 2: Familiarize with Types of Heuristics

• Admissible Heuristics:

  • These heuristics never overestimate the cost to reach the goal.
  • Example: In pathfinding, the straight-line distance to the goal is often used.

• Consistent Heuristics:

  • These ensure that the estimated cost is always less than or equal to the estimated cost from the current node to a neighbor plus the cost of reaching that neighbor.
  • This is essential for algorithms like A* to work correctly.

Step 3: Explore Common Heuristic Search Algorithms

1. A Search Algorithm*:

   • Combines features of Dijkstra’s algorithm and greedy best-first search.
   • Uses the formula:

     f(n) = g(n) + h(n)
     

     where:
     • f(n) is the total estimated cost of the cheapest solution through node n.
     • g(n) is the cost from the start node to node n.
     • h(n) is the heuristic estimate from node n to the goal.

2. Greedy Best-First Search:

   • Expands the node that appears to be closest to the goal.
   • Uses only the heuristic function h(n), ignoring the cost to reach the node.

3. Hill Climbing:

   • A local search algorithm that continuously moves towards the direction of increasing elevation (or value).
   • It's prone to getting stuck in local maxima.

Step 4: Analyze the Trade-offs of Heuristic Search

• Efficiency vs. Optimality:

  • Heuristic searches prioritize speed and efficiency, which can sometimes lead to suboptimal solutions.

• Memory Usage:

  • Some algorithms, like A*, require significant memory for storing nodes, while others, like hill climbing, use less.

Step 5: Implement a Simple Heuristic Search

• Choose a Problem: Start with a simple pathfinding problem on a grid.
• Define Heuristic: Use the Manhattan distance as your heuristic:

  def manhattan_distance(point1, point2):
      return abs(point1[0] - point2[0]) + abs(point1[1] - point2[1])
  

• Run the Algorithm: Implement the A* algorithm using your chosen heuristic and test it on your defined problem.

Conclusion

Heuristic search is a vital aspect of artificial intelligence that enhances problem-solving efficiency. By understanding the types of heuristics, the common algorithms, and their trade-offs, you will be better equipped to apply these concepts in real-world scenarios. Continue experimenting with different problems and heuristics to deepen your understanding and enhance your skills in AI applications.